Skip to main content

A python wrapper for toml++

Project description

pytomlpp

Build Status Conda Status PyPI version TOML

You can try this parser online here.

This is an unofficial python wrapper for toml++ (https://marzer.github.io/tomlplusplus/).

Some points you may want to know before use:

  • Using toml++ means that this module is fully compatible with TOML v1.0.0-rc.3.
  • We convert toml structure to native python data structures (dict/list etc.) when parsing, this is more inline with what json module does.
  • The binding is using pybind11.
  • The project is tested using toml-test and pytest.

Example

In [1]: import pytomlpp                                                                                                                                                                                                                                                                            

In [2]: toml_string = 'hello = "世界"'                                                                                                                                                                                                                                                             

In [3]: pytomlpp.loads(toml_string)                                                                                                                                                                                                                                                                
Out[3]: {'hello': '世界'}

In [4]: type(_)                                                                                                                                                                                                                                                                                    
Out[4]: dict

In [6]: pytomlpp.dumps({"你好": "world"})                                                                                                                 
Out[6]: '"你好" = "world"'

Why bother?

There are some exisitng python TOML parser on the market but from my experience they are all purely implemented in python which is a bit slow.

In [1]: import pytomlpp                                                                                                                                                                                                                                                                                                  

In [2]: import toml                                                                                                                                                                                                                                                                                                      

In [3]: def run_parser(parser_func, toml_string): 
   ...:     for i in range(1000): 
   ...:         parser_func(toml_string) 
   ...:                                                                                                                                                                                                                                                                                                                  

In [4]: %timeit run_parser(pytomlpp.loads, toml_string)                                                                                                                                                                                                                                                                                
310 ms ± 56.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [5]: %timeit run_parser(toml.loads, toml_string)                                                                                                                                                                                                                                                                                    
3.5 s ± 162 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [6]: pytomlpp.lib_version                                                                                                                              
Out[6]: '1.3.2'

Installing

We recommand you to use pip to install this package:

pip install pytomlpp

You can also use conda to install this package, Note we only support linux 64 python 3.8 for now, I would love to provide this package on more python versions and platforms via conda but I have not found a way yet to automate this in the CI, if you know how to do this please contribute!

conda install -c dorafmon pytomlpp

You can also install from source:

git clone git@github.com:bobfang1992/pytomlpp.git --recurse-submodules=third_party/tomlplusplus --shallow-submodules
cd pytomlpp
pip install .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pytomlpp-0.3.4-cp39-cp39-win_amd64.whl (166.7 kB view details)

Uploaded CPython 3.9 Windows x86-64

pytomlpp-0.3.4-cp39-cp39-manylinux2010_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64

pytomlpp-0.3.4-cp39-cp39-manylinux2010_i686.whl (2.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686

pytomlpp-0.3.4-cp39-cp39-macosx_10_9_x86_64.whl (175.1 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pytomlpp-0.3.4-cp38-cp38-win_amd64.whl (166.1 kB view details)

Uploaded CPython 3.8 Windows x86-64

pytomlpp-0.3.4-cp38-cp38-manylinux2010_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

pytomlpp-0.3.4-cp38-cp38-manylinux2010_i686.whl (2.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686

pytomlpp-0.3.4-cp38-cp38-macosx_10_9_x86_64.whl (174.5 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pytomlpp-0.3.4-cp37-cp37m-win_amd64.whl (167.5 kB view details)

Uploaded CPython 3.7m Windows x86-64

pytomlpp-0.3.4-cp37-cp37m-manylinux2010_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

pytomlpp-0.3.4-cp37-cp37m-manylinux2010_i686.whl (2.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686

pytomlpp-0.3.4-cp37-cp37m-macosx_10_9_x86_64.whl (173.7 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

pytomlpp-0.3.4-cp36-cp36m-win_amd64.whl (167.5 kB view details)

Uploaded CPython 3.6m Windows x86-64

pytomlpp-0.3.4-cp36-cp36m-manylinux2010_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

pytomlpp-0.3.4-cp36-cp36m-manylinux2010_i686.whl (2.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ i686

pytomlpp-0.3.4-cp36-cp36m-macosx_10_9_x86_64.whl (173.6 kB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

pytomlpp-0.3.4-cp35-cp35m-win_amd64.whl (167.5 kB view details)

Uploaded CPython 3.5m Windows x86-64

pytomlpp-0.3.4-cp35-cp35m-manylinux2010_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ x86-64

pytomlpp-0.3.4-cp35-cp35m-manylinux2010_i686.whl (2.5 MB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ i686

pytomlpp-0.3.4-cp35-cp35m-macosx_10_9_x86_64.whl (173.6 kB view details)

Uploaded CPython 3.5m macOS 10.9+ x86-64

File details

Details for the file pytomlpp-0.3.4-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 166.7 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 1731ea6b00736ede7d2ce87c91ffb887b7a09f7970b8ec964be882ff151c6d9a
MD5 fdc0aae311a7ad35503b9322009c554b
BLAKE2b-256 bc52c4925b1b55219fbcfd479102ea07e33cefc13e20f6f78b0997476b25c2b1

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp39-cp39-manylinux2010_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp39-cp39-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.9, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp39-cp39-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5c85edc81343122a65c7ec31f51d293033023035d8b3f6f61bfa1b1a5c0fcd7c
MD5 c238cd135cb94620839b7edee4cb8ecf
BLAKE2b-256 bc9b1d9afaf857a122291a11210ec3fd004884f3c2c5083910c250fee70bd083

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp39-cp39-manylinux2010_i686.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp39-cp39-manylinux2010_i686.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.9, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp39-cp39-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 64ab672399b33e2fc21c6a48cab2b0e8ef7689f369c1016573995f665f2dafa9
MD5 d03b30962d8f9bb902a1c16fab07d6b7
BLAKE2b-256 664bf6f372b9afdc369f71486f182c025f9320055080b534f2e34c5552f50d8c

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 175.1 kB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3e9866497bc78eff936b0307a484bd0568f05ca683eb8615168b55da92d32266
MD5 ba3e8221f6ce3b7b69accc16382cd16a
BLAKE2b-256 5db37f7a09bfb01059ad079d71591a7ab876051777015d54b7485456a7f3b65f

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 166.1 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 118682630bd46ef380f8ee9f378ea6d362bf0065bbd0d5ad874509936f2f81e0
MD5 d9e2584d25bcd1e48f8e23934e4cc5d4
BLAKE2b-256 71a8cbf18b635586fc650a71b10c87c2aa77301ed2a79099294ca5f39e92030c

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 f610df219e69d73f47b3ed7b8f1e420f74b7ccd7e23c2d5ada33c5c36029504f
MD5 2e8bcd42387c54a0ee67d3b8d1ef237e
BLAKE2b-256 7b64daf665489f68147302f8fe2e86c8de880b470092791a5958fcd892f5f0f4

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp38-cp38-manylinux2010_i686.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp38-cp38-manylinux2010_i686.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp38-cp38-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 8e747b4a7c7223b8e16369ee9912a3fbd43304806fa69ebb70b3f7ab9dd414dd
MD5 de4462ec5d377b4bf7bdf2564e217429
BLAKE2b-256 f4dbd3bd2c8556a2f7dacc81e8bdcced14368df0e3b8de29a488d0e6997dc1fa

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 174.5 kB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e03e1dbe3cec41482089e5df1413fd46a6f908e6d284ead806da6f385be70284
MD5 d55fbd3fcbfd94685b81fa8b5f5e8bf3
BLAKE2b-256 e5666d8ab6b6f9a6a9fe6cc1898baa157c95157136fa59ac5ecc2ebca5fc42bb

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 167.5 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 35bc9b8508793e3be734baebae353afdd5e6ef0c69a3a03053301012d7fa361f
MD5 627367fceeaff79bb34ce1ac6fe0284f
BLAKE2b-256 a8e88d9634bab9937b6ee53b9b2f55a60d9bc01ec50f5439b1a432b3d624f3a0

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 0cf89f1da5a816ccbf6422a14dec3f8b28b329c9d89b210ece01d466400d2569
MD5 3d05e640e9b74d1079d11f62007f5709
BLAKE2b-256 1e7613cb74ec21bb104772860bfbe3ae25ed31e09d7f91b58899607fafacb950

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp37-cp37m-manylinux2010_i686.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp37-cp37m-manylinux2010_i686.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp37-cp37m-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 a8762de85bf9a019c6395212e86c63b61e916883dbb13691abc1a48a09849cf5
MD5 ba08ca83545e2c3db2dbcf95824f679d
BLAKE2b-256 4528224427b48b60225186d100e7619b234fba373220a93fc5ddd9447d6e3e94

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 173.7 kB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 497e247f2dd7acedbf6e40873be663193a73daab2091cd02342f48e475dfd0e0
MD5 6ee23cf44874504fccecde6bd2987863
BLAKE2b-256 1d8c5554b1526b7d9c214ef7af8280b2bbbec8696515e7cba9ff687a3b0e3350

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 167.5 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 324946016b29c4df129b611950bd6f8d9d006bf98091a53ffe631fddc58b72ee
MD5 71f7afefad608c06d532cfeb0abce2ce
BLAKE2b-256 5902b5f8b0112b9ac8716cbd85310d30430358ecd15a793caca8735c57a04fbd

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 277dcbaa6046229924db65f40cfd37a2b9aa1a968ca2dc65e82ed8fb01bb983b
MD5 daff80802be79bad5534073c1a96196e
BLAKE2b-256 06d94f4f8b248283df371b0981c2fc05e8254333345a010a139de5ab74c1d546

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp36-cp36m-manylinux2010_i686.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp36-cp36m-manylinux2010_i686.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp36-cp36m-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 3ba1706f41a9c8b3412d447ce9804b286cc42506a6766f5c27494e72ef0b076a
MD5 f99c4151250e1ffeeaa2efe931a123db
BLAKE2b-256 525e19b1e30b125c4ebea82f592dd1d10ff1e6f3a4be11f49b53d9fb97170d76

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 173.6 kB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9b3e4c39ce83237d097fafd1155bd6e385aa34ed9bd6952e1e239b93a3081ce3
MD5 e8165965825ff4dc93cc7673eb06fecb
BLAKE2b-256 d352567ae088ffbd49ae24dbc301242f2ac83d6c9db0bf8130003027a120e3b2

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 167.5 kB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 4d68662460201bff14c19f254212a48c0f85abfe027a4e13470cadb3ce28e378
MD5 95ebd5b211948a0a2cbfbb3f8d8148b8
BLAKE2b-256 0db33fe21fc511c308e43603eae7d635882c707464b616d7675c430b74941a97

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp35-cp35m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.5m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 8a14bd2007f37b57107e85531696d77292d82f9bebfe891cf14eaaebdc3ad0fe
MD5 f615cff8e572ab4c91b461ffe0e0b48e
BLAKE2b-256 fb85b68b8cb6a3bf3cb082ea92c9f8a18fa78a3101fb7c4928b12e5e20c07fa5

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp35-cp35m-manylinux2010_i686.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp35-cp35m-manylinux2010_i686.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.5m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp35-cp35m-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 7b0313706c635055074d3f5df00d5af6345a4a39bde1ab206be679ce766882dd
MD5 a7de26b1cf50ccc6681ba0307d7236de
BLAKE2b-256 8e865fbb5232d7255e1011191c57fbbef5ae88f73d1fe6165d6db343b9501f16

See more details on using hashes here.

File details

Details for the file pytomlpp-0.3.4-cp35-cp35m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pytomlpp-0.3.4-cp35-cp35m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 173.6 kB
  • Tags: CPython 3.5m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.0 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.8.7

File hashes

Hashes for pytomlpp-0.3.4-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0edf57d6a3902df2256b42ad2d1993763bd100df030c049c51dd40ab27d63f9c
MD5 076ed5f36b1816ae4a8b9d01b0bbc44a
BLAKE2b-256 f2b66939c384ea67e81b2d4705ea1a3c4012f60362a41f9e0e93ad3e55a0b9ab

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page